Extrinsic Calibration of Visual and Inertial Sensors for the Autonomous Vehicle

The extrinsic calibration of visual-inertial sensors has always been a complex problem for autonomous vehicles because the vehicle usually moves on a plane, rendering the calibration problem degenerate. Even if the vehicle drives on a slope, many great works are ineffective due to insufficient excitation. To address these challenges, we propose a method for calibrating the extrinsic parameters of camera and inertial measurement unit (IMU) sensors for vehicles, which involves two key contributions. The first is the introduction of a virtual vehicle reference frame, acting as a bridge to calibrate rotation in degenerate cases. This involves formulating two least-squares problems to estimate the 3-D relative rotation of the camera-IMU. Second, we propose an approach for estimating camera-IMU translation when a vehicle is navigating on a slope. This is done by solving a linear system, and these values are then refined using an algorithm that takes into account the gravitational magnitude. Extensive experimental results show that our proposed method effectively estimates rotation in planar motion and translation in sloping motion. Moreover, it achieves competitive accuracy and consistency when compared to state-of-the-art approaches.